数字农科院2.0

Early detection of Citrus Huanglongbing by UAV remote sensing based on MGA-UNet

文献类型： 外文期刊

作者： Naibo Ye;Wenyong Mai;Feng Qin;Sen Yuan;Bo Liu;Zaiyuan Li;Conghui Liu;Fanghao Wan;Wanqiang Qian;Zhongzhen Wu;Xi Qiao

作者机构：

关键词： （1-1-1）citrus greening;Citrus HuangLongBing;deep learning;generative model;multispectral images;UAV

期刊名称： Frontiers in Plant Science

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCIE（2025版）

摘要： Citrus Huanglongbing (HLB), also known as citrus greening, is a severe disease that has caused substantial economic damage to the global citrus industry. Early detection is challenging due to the lack of distinctive early symptoms, making current diagnostic methods often ineffective. Therefore, there is an urgent need for an intelligent and timely detection system for HLB. This study leverages multispectral imagery acquired via unmanned aerial vehicles (UAVs) and deep convolutional neural networks. This study introduce a novel model, MGA-UNet, specifically designed for HLB recognition. This image segmentation model enhances feature transmission by integrating channel attention and spatial attention within the skip connections. Furthermore, this study evaluate the comparative effectiveness of high-resolution and multispectral images in HLB detection, finding that multispectral imagery offers superior performance. To address data imbalance and augment the dataset, this study employ a generative model, DCGAN, for data augmentation, significantly boosting the model’s recognition accuracy. Our proposed model achieved a mIoU of 0.89, a mPA of 0.94, a precision of 0.95, and a recall of 0.94 in identifying diseased trees. The intelligent monitoring method for HLB presented in this study offers a cost-effective and highly accurate solution, holding considerable promise for the early warning of this disease.

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